The 5 Obstacles in the Way of Automation

Automation has unrivaled potential to change our global workforce forever. Already, millions of workers (and some entire industries) have lost their jobs due to basic types of automation, like assembly line robots or search algorithms. In the next 13 years or so, we may lose up to 800 million more.

We’re making astounding progress in the realm of machine learning, AI, and automation, to the point where some technological optimists are already making plans for a fully jobless future. But at the same time, we’re years to decades away from automating even some of the more basic professional jobs.

Why? Because there are fundamental limitations preventing automation from moving forward.

Why Aren’t We Automating Everything?

So what’s really standing in the way of us making more progress in the automation space?

1. Job complexity. The most obvious stopping point is the sheer complexity of certain types of jobs. Basic tasks, which always follow the same routine, and have clear answers to every question that arises, are easy to automate with a basic algorithm. But jobs that require more ambiguous types of decision-making, such as incorporating human emotions and indefinable instincts, are much more difficult to tackle. The problem grows even more complex when human interactions are required.

2. Processing power. Machines need more data than humans do to “learn” a task. And we’re not just talking slight differences here; they need hundreds of thousands of times more raw data than humans need. Image and video recognition programs need to be fed millions of examples before they truly understand their subjects, and that eats up a ton of processing power. Accordingly, advanced machine learning algorithms (and the capacity for automation) require enormous servers, which are impractical for ground-level applications.

3. Consumer adoption and trust. Automated tools are already available for millions of tasks, from sending out batches of SMS texts to regulating pharmaceuticals. But consumers aren’t necessarily ready to adopt them. For example, many modern consumers hate the idea of riding in a self-driving car, because it means surrendering control to a machine they don’t fully trust. If customers aren’t willing to buy or support a new type of automation, companies and innovators aren’t going to be as interested in pursuing it.

4. Specialization vs. generalization. Today, we have the technology to create AI-driven, automated solutions for all kinds of problems—but very specific ones. Specialized AI can be custom-made to “understand” a certain topic, or behave in exactly the right way to make one type of decision, but creating a generalized AI, which can make more complex decisions and be applied to many different disciplines, is much harder. We’re decades away from seeing the rise of a truly successful general AI, which means every form of automation we’ll have for the next few years will be hyper-focused on one (or a few related) tasks.

5. Legal regulations. For some types of automation, legal regulations can also be a problem. For example, autonomous driving algorithms have come a long way, but industry moguls and regulators are still concerned about how those programs could be applied to the trucking industry. For roles that pose an inherent risk to human life or health, lawmakers are taking automation seriously.

How Fast Can We Grow?

Despite these challenges, experts in machine learning, innovative engineers, and visionary entrepreneurs are all competing to see who can come up with the next world-changing device or program. Each year, we see rise to new AI breakthroughs and gadgets in almost every industry, making iterative progress toward what could really be a jobless future.

Obstacles related to human concerns and intervention, such as overcoming legal, regulatory, and consumer adoption barriers, are the biggest impediments here. As for sheer processing power, and coming up with solutions for the most difficult jobs to automate, it’s hard to imagine we’re more than a few years away from a breakthrough—at least with today’s machine learning pace.

In the meantime, we all can take advantage of the automated tools currently within our grasp, and make our jobs a little easier—long before they have a chance of being replaced.